Overview

Dataset statistics

Number of variables15
Number of observations47738
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 MiB
Average record size in memory120.0 B

Variable types

Categorical2
DateTime1
Numeric12

Warnings

iso_code has a high cardinality: 131 distinct values High cardinality
country_region has a high cardinality: 131 distinct values High cardinality
retail_and_recreation_percent_change_from_baseline has 619 (1.3%) zeros Zeros
grocery_and_pharmacy_percent_change_from_baseline has 1398 (2.9%) zeros Zeros
parks_percent_change_from_baseline has 530 (1.1%) zeros Zeros
workplaces_percent_change_from_baseline has 595 (1.2%) zeros Zeros
residential_percent_change_from_baseline has 1747 (3.7%) zeros Zeros
new_cases_per_million has 7580 (15.9%) zeros Zeros
new_cases has 7580 (15.9%) zeros Zeros
new_deaths has 19595 (41.0%) zeros Zeros
new_deaths_per_million has 19595 (41.0%) zeros Zeros

Reproduction

Analysis started2021-03-09 14:34:45.731390
Analysis finished2021-03-09 14:35:19.360318
Duration33.63 seconds
Software versionpandas-profiling v2.12.0
Download configurationconfig.yaml

Variables

iso_code
Categorical

HIGH CARDINALITY

Distinct131
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size373.1 KiB
ARG
 
382
DEU
 
382
FJI
 
382
FRA
 
382
SGP
 
382
Other values (126)
45828 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters143214
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowARE
2nd rowARE
3rd rowARE
4th rowARE
5th rowARE
ValueCountFrequency (%)
ARG382
 
0.8%
DEU382
 
0.8%
FJI382
 
0.8%
FRA382
 
0.8%
SGP382
 
0.8%
PHL382
 
0.8%
KOR382
 
0.8%
LKA382
 
0.8%
FIN382
 
0.8%
ESP382
 
0.8%
Other values (121)43918
92.0%
2021-03-09T14:35:19.698179image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
arg382
 
0.8%
aus382
 
0.8%
phl382
 
0.8%
fin382
 
0.8%
svn382
 
0.8%
npl382
 
0.8%
vnm382
 
0.8%
lka382
 
0.8%
che382
 
0.8%
can382
 
0.8%
Other values (121)43918
92.0%

Most occurring characters

ValueCountFrequency (%)
A12401
 
8.7%
R11733
 
8.2%
N10626
 
7.4%
L8417
 
5.9%
M7972
 
5.6%
B7857
 
5.5%
E7650
 
5.3%
G7516
 
5.2%
T6915
 
4.8%
S6278
 
4.4%
Other values (16)55849
39.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter143214
100.0%

Most frequent character per category

ValueCountFrequency (%)
A12401
 
8.7%
R11733
 
8.2%
N10626
 
7.4%
L8417
 
5.9%
M7972
 
5.6%
B7857
 
5.5%
E7650
 
5.3%
G7516
 
5.2%
T6915
 
4.8%
S6278
 
4.4%
Other values (16)55849
39.0%

Most occurring scripts

ValueCountFrequency (%)
Latin143214
100.0%

Most frequent character per script

ValueCountFrequency (%)
A12401
 
8.7%
R11733
 
8.2%
N10626
 
7.4%
L8417
 
5.9%
M7972
 
5.6%
B7857
 
5.5%
E7650
 
5.3%
G7516
 
5.2%
T6915
 
4.8%
S6278
 
4.4%
Other values (16)55849
39.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII143214
100.0%

Most frequent character per block

ValueCountFrequency (%)
A12401
 
8.7%
R11733
 
8.2%
N10626
 
7.4%
L8417
 
5.9%
M7972
 
5.6%
B7857
 
5.5%
E7650
 
5.3%
G7516
 
5.2%
T6915
 
4.8%
S6278
 
4.4%
Other values (16)55849
39.0%

date
Date

Distinct382
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size373.1 KiB
Minimum2020-02-15 00:00:00
Maximum2021-03-02 00:00:00
2021-03-09T14:35:19.813823image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:19.938287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct375
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-24.75201307
Minimum-100
Maximum62
Zeros619
Zeros (%)1.3%
Negative41616
Negative (%)87.2%
Memory size373.1 KiB
2021-03-09T14:35:20.086014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-71
Q1-39
median-21
Q3-8
95-th percentile8
Maximum62
Range162
Interquartile range (IQR)31

Descriptive statistics

Standard deviation24.03599259
Coefficient of variation (CV)-0.9710722323
Kurtosis0.202705403
Mean-24.75201307
Median Absolute Deviation (MAD)15
Skewness-0.4510691642
Sum-1181611.6
Variance577.7289396
MonotocityNot monotonic
2021-03-09T14:35:20.218896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-201013
 
2.1%
-22971
 
2.0%
-18963
 
2.0%
-21913
 
1.9%
-19912
 
1.9%
-15909
 
1.9%
-12886
 
1.9%
-17885
 
1.9%
-10885
 
1.9%
-14881
 
1.8%
Other values (365)38520
80.7%
ValueCountFrequency (%)
-1002
 
< 0.1%
-9711
 
< 0.1%
-9622
< 0.1%
-9531
0.1%
-9422
< 0.1%
ValueCountFrequency (%)
622
< 0.1%
601
 
< 0.1%
591
 
< 0.1%
582
< 0.1%
573
< 0.1%
Distinct401
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.202832125
Minimum-100
Maximum162
Zeros1398
Zeros (%)2.9%
Negative29565
Negative (%)61.9%
Memory size373.1 KiB
2021-03-09T14:35:20.363881image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-45
Q1-16
median-5
Q34
95-th percentile28
Maximum162
Range262
Interquartile range (IQR)20

Descriptive statistics

Standard deviation22.19363466
Coefficient of variation (CV)-3.577984091
Kurtosis2.937448808
Mean-6.202832125
Median Absolute Deviation (MAD)10
Skewness-0.3223534239
Sum-296110.8
Variance492.5574196
MonotocityNot monotonic
2021-03-09T14:35:20.494243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01398
 
2.9%
-21395
 
2.9%
-11381
 
2.9%
11372
 
2.9%
-31356
 
2.8%
-41334
 
2.8%
-51296
 
2.7%
21293
 
2.7%
-71222
 
2.6%
-61188
 
2.5%
Other values (391)34503
72.3%
ValueCountFrequency (%)
-1002
 
< 0.1%
-982
 
< 0.1%
-9714
< 0.1%
-9610
< 0.1%
-9522
< 0.1%
ValueCountFrequency (%)
1621
< 0.1%
1601
< 0.1%
1481
< 0.1%
1271
< 0.1%
1221
< 0.1%
Distinct685
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.915166115
Minimum-100
Maximum517
Zeros530
Zeros (%)1.1%
Negative31335
Negative (%)65.6%
Memory size373.1 KiB
2021-03-09T14:35:20.622072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-57
Q1-28
median-12
Q39
95-th percentile82
Maximum517
Range617
Interquartile range (IQR)37

Descriptive statistics

Standard deviation47.26114706
Coefficient of variation (CV)-16.2121626
Kurtosis11.30416713
Mean-2.915166115
Median Absolute Deviation (MAD)18
Skewness2.500724359
Sum-139164.2
Variance2233.616021
MonotocityNot monotonic
2021-03-09T14:35:20.735287image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-15819
 
1.7%
-18787
 
1.6%
-19779
 
1.6%
-16774
 
1.6%
-17766
 
1.6%
-22750
 
1.6%
-14739
 
1.5%
-12718
 
1.5%
-21714
 
1.5%
-11706
 
1.5%
Other values (675)40186
84.2%
ValueCountFrequency (%)
-1001
 
< 0.1%
-953
< 0.1%
-943
< 0.1%
-933
< 0.1%
-927
< 0.1%
ValueCountFrequency (%)
5171
< 0.1%
4841
< 0.1%
4731
< 0.1%
4551
< 0.1%
4531
< 0.1%
Distinct375
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-29.15483682
Minimum-100
Maximum91
Zeros413
Zeros (%)0.9%
Negative42362
Negative (%)88.7%
Memory size373.1 KiB
2021-03-09T14:35:20.864840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-70
Q1-45
median-29
Q3-13
95-th percentile9
Maximum91
Range191
Interquartile range (IQR)32

Descriptive statistics

Standard deviation23.9647793
Coefficient of variation (CV)-0.821982968
Kurtosis0.07469423655
Mean-29.15483682
Median Absolute Deviation (MAD)16
Skewness0.0226636942
Sum-1391793.6
Variance574.3106468
MonotocityNot monotonic
2021-03-09T14:35:20.999699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-35846
 
1.8%
-24816
 
1.7%
-30793
 
1.7%
-32793
 
1.7%
-29787
 
1.6%
-34782
 
1.6%
-36775
 
1.6%
-23775
 
1.6%
-26774
 
1.6%
-22758
 
1.6%
Other values (365)39839
83.5%
ValueCountFrequency (%)
-1001
 
< 0.1%
-992
 
< 0.1%
-981
 
< 0.1%
-965
< 0.1%
-9510
< 0.1%
ValueCountFrequency (%)
911
 
< 0.1%
841
 
< 0.1%
811
 
< 0.1%
791
 
< 0.1%
773
< 0.1%
Distinct193
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.85745109
Minimum-99
Maximum80
Zeros595
Zeros (%)1.2%
Negative42605
Negative (%)89.2%
Memory size373.1 KiB
2021-03-09T14:35:21.136427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-61
Q1-33
median-21
Q3-9
95-th percentile5
Maximum80
Range179
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.4574084
Coefficient of variation (CV)-0.8512501384
Kurtosis0.5074004387
Mean-22.85745109
Median Absolute Deviation (MAD)12
Skewness-0.6042455305
Sum-1091169
Variance378.5907417
MonotocityNot monotonic
2021-03-09T14:35:21.256072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-241115
 
2.3%
-201093
 
2.3%
-181089
 
2.3%
-261085
 
2.3%
-231064
 
2.2%
-121056
 
2.2%
-251051
 
2.2%
-161045
 
2.2%
-171040
 
2.2%
-211040
 
2.2%
Other values (183)37060
77.6%
ValueCountFrequency (%)
-992
 
< 0.1%
-941
 
< 0.1%
-922
 
< 0.1%
-913
 
< 0.1%
-9017
< 0.1%
ValueCountFrequency (%)
801
< 0.1%
741
< 0.1%
711
< 0.1%
672
< 0.1%
561
< 0.1%
Distinct194
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.613754242
Minimum-35
Maximum55
Zeros1747
Zeros (%)3.7%
Negative4139
Negative (%)8.7%
Memory size373.1 KiB
2021-03-09T14:35:21.399389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-35
5-th percentile-2
Q13
median8
Q314
95-th percentile27
Maximum55
Range90
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.844899105
Coefficient of variation (CV)0.9200255053
Kurtosis1.01201556
Mean9.613754242
Median Absolute Deviation (MAD)5
Skewness0.8403260319
Sum458941.4
Variance78.23224017
MonotocityNot monotonic
2021-03-09T14:35:21.517110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102352
 
4.9%
52342
 
4.9%
92329
 
4.9%
42301
 
4.8%
72255
 
4.7%
62252
 
4.7%
82250
 
4.7%
32190
 
4.6%
112189
 
4.6%
22169
 
4.5%
Other values (184)25109
52.6%
ValueCountFrequency (%)
-351
< 0.1%
-331
< 0.1%
-211
< 0.1%
-162
< 0.1%
-151
< 0.1%
ValueCountFrequency (%)
552
< 0.1%
541
< 0.1%
531
< 0.1%
521
< 0.1%
512
< 0.1%

country_region
Categorical

HIGH CARDINALITY

Distinct131
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size373.1 KiB
Argentina
 
382
United States
 
382
Nepal
 
382
Egypt
 
382
Singapore
 
382
Other values (126)
45828 

Length

Max length22
Median length7
Mean length8.126817211
Min length4

Characters and Unicode

Total characters387958
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited Arab Emirates
2nd rowUnited Arab Emirates
3rd rowUnited Arab Emirates
4th rowUnited Arab Emirates
5th rowUnited Arab Emirates
ValueCountFrequency (%)
Argentina382
 
0.8%
United States382
 
0.8%
Nepal382
 
0.8%
Egypt382
 
0.8%
Singapore382
 
0.8%
Australia382
 
0.8%
Malta382
 
0.8%
Morocco382
 
0.8%
South Africa382
 
0.8%
Italy382
 
0.8%
Other values (121)43918
92.0%
2021-03-09T14:35:21.803858image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united1146
 
2.0%
and1066
 
1.9%
south764
 
1.3%
new714
 
1.2%
egypt382
 
0.7%
spain382
 
0.7%
finland382
 
0.7%
fiji382
 
0.7%
australia382
 
0.7%
sweden382
 
0.7%
Other values (141)51189
89.5%

Most occurring characters

ValueCountFrequency (%)
a64202
16.5%
i33422
 
8.6%
n29472
 
7.6%
e26915
 
6.9%
r20808
 
5.4%
o19334
 
5.0%
t14311
 
3.7%
u13331
 
3.4%
l13263
 
3.4%
d12818
 
3.3%
Other values (46)140082
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter320745
82.7%
Uppercase Letter56423
 
14.5%
Space Separator9433
 
2.4%
Other Punctuation357
 
0.1%
Open Punctuation341
 
0.1%
Close Punctuation341
 
0.1%
Dash Punctuation318
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a64202
20.0%
i33422
10.4%
n29472
9.2%
e26915
 
8.4%
r20808
 
6.5%
o19334
 
6.0%
t14311
 
4.5%
u13331
 
4.2%
l13263
 
4.1%
d12818
 
4.0%
Other values (17)72869
22.7%
ValueCountFrequency (%)
B6044
 
10.7%
S5210
 
9.2%
M3997
 
7.1%
C3667
 
6.5%
N3633
 
6.4%
A3291
 
5.8%
T3204
 
5.7%
L2917
 
5.2%
P2914
 
5.2%
G2832
 
5.0%
Other values (14)18714
33.2%
ValueCountFrequency (%)
9433
100.0%
ValueCountFrequency (%)
'357
100.0%
ValueCountFrequency (%)
-318
100.0%
ValueCountFrequency (%)
(341
100.0%
ValueCountFrequency (%)
)341
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin377168
97.2%
Common10790
 
2.8%

Most frequent character per script

ValueCountFrequency (%)
a64202
17.0%
i33422
 
8.9%
n29472
 
7.8%
e26915
 
7.1%
r20808
 
5.5%
o19334
 
5.1%
t14311
 
3.8%
u13331
 
3.5%
l13263
 
3.5%
d12818
 
3.4%
Other values (41)129292
34.3%
ValueCountFrequency (%)
9433
87.4%
'357
 
3.3%
(341
 
3.2%
)341
 
3.2%
-318
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII387601
99.9%
None357
 
0.1%

Most frequent character per block

ValueCountFrequency (%)
a64202
16.6%
i33422
 
8.6%
n29472
 
7.6%
e26915
 
6.9%
r20808
 
5.4%
o19334
 
5.0%
t14311
 
3.7%
u13331
 
3.4%
l13263
 
3.4%
d12818
 
3.3%
Other values (45)139725
36.0%
ValueCountFrequency (%)
ô357
100.0%

new_cases_per_million
Real number (ℝ)

ZEROS

Distinct25259
Distinct (%)52.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.18447907
Minimum-2153.437
Maximum3475.672
Zeros7580
Zeros (%)15.9%
Negative46
Negative (%)0.1%
Memory size373.1 KiB
2021-03-09T14:35:21.939759image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2153.437
5-th percentile0
Q10.612
median10.206
Q371.54675
95-th percentile349.34725
Maximum3475.672
Range5629.109
Interquartile range (IQR)70.93475

Descriptive statistics

Standard deviation160.2342026
Coefficient of variation (CV)2.219787477
Kurtosis43.43163113
Mean72.18447907
Median Absolute Deviation (MAD)10.206
Skewness4.920596285
Sum3445942.662
Variance25674.9997
MonotocityNot monotonic
2021-03-09T14:35:22.333438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07580
 
15.9%
0.04271
 
0.1%
0.20768
 
0.1%
3.4867
 
0.1%
0.0161
 
0.1%
0.04160
 
0.1%
0.41558
 
0.1%
0.0652
 
0.1%
0.30549
 
0.1%
0.03449
 
0.1%
Other values (25249)39623
83.0%
ValueCountFrequency (%)
-2153.4371
< 0.1%
-1590.1471
< 0.1%
-705.8911
< 0.1%
-450.7721
< 0.1%
-261.5761
< 0.1%
ValueCountFrequency (%)
3475.6721
< 0.1%
3216.5691
< 0.1%
3205.0831
< 0.1%
3142.2931
< 0.1%
2533.4451
< 0.1%

new_cases
Real number (ℝ)

ZEROS

Distinct7568
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2309.98674
Minimum-74347
Maximum299786
Zeros7580
Zeros (%)15.9%
Negative46
Negative (%)0.1%
Memory size373.1 KiB
2021-03-09T14:35:22.469280image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-74347
5-th percentile0
Q17
median132
Q3904
95-th percentile9300.15
Maximum299786
Range374133
Interquartile range (IQR)897

Descriptive statistics

Standard deviation10830.40149
Coefficient of variation (CV)4.688512407
Kurtosis223.7322348
Mean2309.98674
Median Absolute Deviation (MAD)132
Skewness12.85179988
Sum110274147
Variance117297596.5
MonotocityNot monotonic
2021-03-09T14:35:22.594990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07580
 
15.9%
11259
 
2.6%
2830
 
1.7%
3632
 
1.3%
4550
 
1.2%
5507
 
1.1%
6456
 
1.0%
7416
 
0.9%
8360
 
0.8%
9334
 
0.7%
Other values (7558)34814
72.9%
ValueCountFrequency (%)
-743471
< 0.1%
-460761
< 0.1%
-170741
< 0.1%
-100341
< 0.1%
-79531
< 0.1%
ValueCountFrequency (%)
2997861
< 0.1%
2921051
< 0.1%
2770681
< 0.1%
2623371
< 0.1%
2539931
< 0.1%

new_deaths
Real number (ℝ)

ZEROS

Distinct1268
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.26836064
Minimum-1918
Maximum4465
Zeros19595
Zeros (%)41.0%
Negative66
Negative (%)0.1%
Memory size373.1 KiB
2021-03-09T14:35:22.735733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1918
5-th percentile0
Q10
median2
Q316
95-th percentile242
Maximum4465
Range6383
Interquartile range (IQR)16

Descriptive statistics

Standard deviation204.7244611
Coefficient of variation (CV)3.993193045
Kurtosis121.2906125
Mean51.26836064
Median Absolute Deviation (MAD)2
Skewness9.156573558
Sum2447449
Variance41912.10497
MonotocityNot monotonic
2021-03-09T14:35:22.853647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019595
41.0%
13730
 
7.8%
22279
 
4.8%
31594
 
3.3%
41383
 
2.9%
51143
 
2.4%
6910
 
1.9%
7741
 
1.6%
8728
 
1.5%
9602
 
1.3%
Other values (1258)15033
31.5%
ValueCountFrequency (%)
-19181
< 0.1%
-4431
< 0.1%
-2321
< 0.1%
-2171
< 0.1%
-1171
< 0.1%
ValueCountFrequency (%)
44651
< 0.1%
43931
< 0.1%
41711
< 0.1%
41431
< 0.1%
40831
< 0.1%

new_deaths_per_million
Real number (ℝ)

ZEROS

Distinct5477
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.394059701
Minimum-67.901
Maximum218.329
Zeros19595
Zeros (%)41.0%
Negative66
Negative (%)0.1%
Memory size373.1 KiB
2021-03-09T14:35:22.980775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-67.901
5-th percentile0
Q10
median0.106
Q31.151
95-th percentile7.2483
Maximum218.329
Range286.23
Interquartile range (IQR)1.151

Descriptive statistics

Standard deviation3.689566427
Coefficient of variation (CV)2.646634448
Kurtosis430.1261304
Mean1.394059701
Median Absolute Deviation (MAD)0.106
Skewness12.2031928
Sum66549.622
Variance13.61290042
MonotocityNot monotonic
2021-03-09T14:35:23.100089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019595
41.0%
0.288125
 
0.3%
0.588124
 
0.3%
0.032123
 
0.3%
0.202121
 
0.3%
0.039115
 
0.2%
0.101112
 
0.2%
0.347111
 
0.2%
0.093108
 
0.2%
0.034100
 
0.2%
Other values (5467)27104
56.8%
ValueCountFrequency (%)
-67.9011
< 0.1%
-41.0231
< 0.1%
-22.9721
< 0.1%
-12.2481
< 0.1%
-10.0951
< 0.1%
ValueCountFrequency (%)
218.3291
< 0.1%
141.8651
< 0.1%
125.6531
< 0.1%
117.8881
< 0.1%
111.4311
< 0.1%

latitude
Real number (ℝ)

Distinct131
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.02888733
Minimum-40.900557
Maximum61.92411
Zeros0
Zeros (%)0.0%
Negative9011
Negative (%)18.9%
Memory size373.1 KiB
2021-03-09T14:35:23.238557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-40.900557
5-th percentile-23.442503
Q17.873054
median23.634501
Q343.915886
95-th percentile56.26392
Maximum61.92411
Range102.824667
Interquartile range (IQR)36.042832

Descriptive statistics

Standard deviation24.95775874
Coefficient of variation (CV)1.13295594
Kurtosis-0.4655304742
Mean22.02888733
Median Absolute Deviation (MAD)17.974134
Skewness-0.4875528992
Sum1051615.023
Variance622.8897213
MonotocityNot monotonic
2021-03-09T14:35:23.374663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.352083382
 
0.8%
28.394857382
 
0.8%
12.565679382
 
0.8%
36.204824382
 
0.8%
12.879721382
 
0.8%
-25.274398382
 
0.8%
60.128161382
 
0.8%
56.130366382
 
0.8%
15.870032382
 
0.8%
41.87194382
 
0.8%
Other values (121)43918
92.0%
ValueCountFrequency (%)
-40.900557369
0.8%
-38.416097382
0.8%
-35.675147374
0.8%
-32.522779355
0.7%
-30.559482382
0.8%
ValueCountFrequency (%)
61.92411382
0.8%
61.52401382
0.8%
60.472024374
0.8%
60.128161382
0.8%
58.595272371
0.8%

longitude
Real number (ℝ)

Distinct131
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.03663629
Minimum-106.346771
Maximum178.065032
Zeros0
Zeros (%)0.0%
Negative14097
Negative (%)29.5%
Memory size373.1 KiB
2021-03-09T14:35:23.500534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-106.346771
5-th percentile-86.241905
Q1-5.54708
median19.145136
Q348.516388
95-th percentile121.774017
Maximum178.065032
Range284.411803
Interquartile range (IQR)54.063468

Descriptive statistics

Standard deviation62.01120499
Coefficient of variation (CV)3.438069272
Kurtosis-0.1984005117
Mean18.03663629
Median Absolute Deviation (MAD)27.36959
Skewness0.06277719199
Sum861032.9431
Variance3845.389544
MonotocityNot monotonic
2021-03-09T14:35:23.618427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.435973382
 
0.8%
25.748151382
 
0.8%
18.643501382
 
0.8%
103.819836382
 
0.8%
133.775136382
 
0.8%
78.96288382
 
0.8%
14.375416382
 
0.8%
80.771797382
 
0.8%
104.990963382
 
0.8%
127.766922382
 
0.8%
Other values (121)43918
92.0%
ValueCountFrequency (%)
-106.346771382
0.8%
-102.552784382
0.8%
-95.712891382
0.8%
-90.230759380
0.8%
-88.89653349
0.7%
ValueCountFrequency (%)
178.065032382
0.8%
174.885971369
0.8%
143.95555345
0.7%
138.252924382
0.8%
133.775136382
0.8%

Interactions

2021-03-09T14:35:00.288387image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:00.473543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:00.630081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:00.800910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:00.948651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:01.094223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:01.228532image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:01.371646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:01.524036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:01.661404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:01.795107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:01.934843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:02.070734image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:02.201984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:02.340388image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:02.476772image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:02.593920image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:02.713301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:02.847608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:02.967279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:03.099088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:03.219970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:03.515368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:03.661166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:03.788196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:03.936806image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:04.075553image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:04.198664image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:04.331292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:04.472342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:04.596929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:04.731036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:04.850190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:04.983262image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:05.133296image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:05.280530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:05.426121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:05.575614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:05.703868image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:05.842304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:05.984989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:06.132857image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:06.272148image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:06.454318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:06.596936image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:06.746325image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:06.895011image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:07.032128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:07.180970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:07.311972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:07.444426image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:07.582832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:07.714277image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:07.845731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:07.985520image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:08.116260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:08.251369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:08.364457image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:08.481999image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:08.610222image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:08.728746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:08.845301image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:08.976606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:09.271471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:09.391677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:09.513238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:09.627940image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:09.771010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:09.901571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:10.036368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:10.168298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:10.300820image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:10.424140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:10.559166image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:10.677751image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:10.792683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:10.917655image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:11.040298image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:11.188625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:11.325544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:11.471488image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:11.623891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:11.758444image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:11.877317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:12.019586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:12.155824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:12.282207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:12.416480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:12.555757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:12.688833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:12.813075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:12.953818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:13.087051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:13.262441image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:13.381619image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:13.504447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:13.647552image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:13.770206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:13.888381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:14.017885image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:14.171004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:14.313514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:14.461042image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:14.613640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:14.747536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:14.861783image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:14.976288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:15.106026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:15.227292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:15.350610image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:15.474196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:15.620633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:15.746899image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:15.874609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:16.199845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:16.328051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:16.445365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:16.570714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:16.691604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:16.814138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:16.937150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:17.058523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:17.205420image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:17.340818image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:17.482308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:17.620152image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:17.755557image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:17.880752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:17.991659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:18.131182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:18.245911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-03-09T14:35:18.357491image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-03-09T14:35:23.740127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-09T14:35:23.998303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-09T14:35:24.260575image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-09T14:35:24.521975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-03-09T14:35:18.618666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-09T14:35:19.043614image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

iso_codedateretail_and_recreation_percent_change_from_baselinegrocery_and_pharmacy_percent_change_from_baselineparks_percent_change_from_baselinetransit_stations_percent_change_from_baselineworkplaces_percent_change_from_baselineresidential_percent_change_from_baselinecountry_regionnew_cases_per_millionnew_casesnew_deathsnew_deaths_per_millionlatitudelongitude
0ARE2020-02-150.04.05.00.02.01.0United Arab Emirates0.0000.00.00.023.42407653.847818
1ARE2020-02-161.04.04.01.02.01.0United Arab Emirates0.1011.00.00.023.42407653.847818
2ARE2020-02-17-1.01.05.01.02.01.0United Arab Emirates0.0000.00.00.023.42407653.847818
3ARE2020-02-18-2.01.05.00.02.01.0United Arab Emirates0.0000.00.00.023.42407653.847818
4ARE2020-02-19-2.00.04.0-1.02.01.0United Arab Emirates0.0000.00.00.023.42407653.847818
5ARE2020-02-20-2.01.06.01.01.01.0United Arab Emirates0.0000.00.00.023.42407653.847818
6ARE2020-02-21-3.02.06.00.0-1.01.0United Arab Emirates0.0000.00.00.023.42407653.847818
7ARE2020-02-22-2.02.04.0-2.03.01.0United Arab Emirates0.4044.00.00.023.42407653.847818
8ARE2020-02-23-1.03.03.0-1.04.01.0United Arab Emirates0.0000.00.00.023.42407653.847818
9ARE2020-02-24-3.00.05.0-1.03.01.0United Arab Emirates0.0000.00.00.023.42407653.847818

Last rows

iso_codedateretail_and_recreation_percent_change_from_baselinegrocery_and_pharmacy_percent_change_from_baselineparks_percent_change_from_baselinetransit_stations_percent_change_from_baselineworkplaces_percent_change_from_baselineresidential_percent_change_from_baselinecountry_regionnew_cases_per_millionnew_casesnew_deathsnew_deaths_per_millionlatitudelongitude
47728ZWE2021-02-21-20.06.02.0-33.0-13.09.0Zimbabwe1.88428.04.00.269-19.01543829.154857
47729ZWE2021-02-22-36.0-15.0-17.0-43.0-66.021.0Zimbabwe4.44166.05.00.336-19.01543829.154857
47730ZWE2021-02-23-17.04.0-12.0-26.0-20.010.0Zimbabwe3.23048.07.00.471-19.01543829.154857
47731ZWE2021-02-24-17.08.0-11.0-25.0-21.012.0Zimbabwe3.36450.08.00.538-19.01543829.154857
47732ZWE2021-02-25-13.09.0-12.0-22.0-21.012.0Zimbabwe2.28834.02.00.135-19.01543829.154857
47733ZWE2021-02-26-18.08.0-17.0-27.0-18.012.0Zimbabwe3.36450.05.00.336-19.01543829.154857
47734ZWE2021-02-27-13.019.0-9.0-24.0-7.05.0Zimbabwe0.94214.00.00.000-19.01543829.154857
47735ZWE2021-02-28-21.08.0-15.0-29.0-10.08.0Zimbabwe2.08631.00.00.000-19.01543829.154857
47736ZWE2021-03-01-12.07.0-12.0-21.0-18.010.0Zimbabwe1.74926.05.00.336-19.01543829.154857
47737ZWE2021-03-02-2.026.01.0-11.0-14.08.0Zimbabwe2.22033.04.00.269-19.01543829.154857